-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
684 lines (623 loc) · 30.3 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="Multi-Task Text-Guided Mobile Manipulation with Visual-Force Goals">
<meta name="keywords" content="Force, Imitation Learning, Robotics, Transformers">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>ForceSight: Text-Guided Mobile Manipulation with Visual-Force Goals</title>
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
rel="stylesheet">
<link rel="stylesheet" href="./static/css/bulma.min.css">
<link rel="stylesheet" href="./static/css/bulma-carousel.min.css">
<link rel="stylesheet" href="./static/css/bulma-slider.min.css">
<link rel="stylesheet" href="./static/css/fontawesome.all.min.css">
<link rel="stylesheet"
href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
<link rel="stylesheet" href="./static/css/index.css">
<!-- <link rel="icon" href="./static/images/favicon.svg"> -->
<link rel="icon" href="./static/images/forcesight_logo.png">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script defer src="./static/js/fontawesome.all.min.js"></script>
<script src="./static/js/bulma-carousel.min.js"></script>
<script src="./static/js/bulma-slider.min.js"></script>
<!-- <script src="./static/js/index.js"></script> -->
<!-- Google tag (gtag.js) -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-4V98WMNGBD"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-4V98WMNGBD');
</script>
</head>
<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">ForceSight: Text-Guided Mobile Manipulation with Visual-Force Goals</h1>
<div class="subtitle is-3 publication-venue">
<h2 style="font-weight: bolder">ICRA 2024</h2>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://jeremy-collins.github.io/">Jeremy A. Collins</a><sup>*</sup><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://codyhouff.github.io/">Cody Houff</a><sup>*</sup><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://youliangtan.github.io/">You Liang Tan</a><sup>*</sup><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://charliekemp.com/">Charlie C. Kemp</a><sup>1</sup>
</span>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>*</sup>Equal Contribution</span>
<span class="author-block"><sup>1</sup>Georgia Institute of Technology</span>
</div>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link.
<span class="link-block">
<a href="./static/paper/ForceSight.pdf"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span> -->
<!-- arXiv Link. -->
<span class="link-block">
<a href="https://arxiv.org/abs/2309.12312"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>Paper</span>
</a>
</span>
<!-- Video Link. -->
<!-- <span class="link-block">
<a href="https://www.youtube.com/watch?v=MrKrnHhk8IA"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-youtube"></i>
</span>
<span>Video</span>
</a>
</span> -->
<!-- Code Link. -->
<span class="link-block">
<a href="https://github.com/force-sight/forcesight"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- Dataset Link. -->
<span class="link-block">
<a href="https://1drv.ms/f/s!AjebifpxoPl5hO5bu91QCJSDizws9g?e=h9AlnZ"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-database"></i>
</span>
<span>Models/Dataset</span>
</a>
<!-- Appendix Link. -->
<span class="link-block">
<a href="./static/paper/ForceSight.pdf"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Appendix (coming soon!)</span>
</a>
</span>
</div>
<!-- <div style="color: grey; font-style: italic; font-size: small;">Paper last revised: Sept 23 2023</div> -->
</div>
</div>
</div>
</div>
</section>
<!-- <section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<video id="teaser" autoplay muted loop playsinline height="100%">
<source src="./static/videos/keys_cubicle_combined.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
<span class="dnerf">ForceSight</span> proposes visual-force goals for mobile manipulation, enabling a variety of robotic tasks.
</h2>
</div>
</div>
</section> -->
<!-- replacing the above video with an image -->
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<img src="./static/images/Headliner.png" class="interpolation-image is-centered" alt="ForceSight teaser"/>
<h2 class="subtitle has-text-centered">
<span class="dnerf">ForceSight</span> is an RGBD-adapted, text-conditioned vision transformer. Given an RGBD image and a text prompt, <span class="dnerf">ForceSight</span> produces visual-force
goals for a mobile manipulator. Action primitives, shown below each image, are appended to the text input by a simple low-level controller.
</h2>
</div>
</div>
<!-- <section class="hero is-light is-small">
<div class="hero-body">
<div class="container">
<div id="results-carousel" class="carousel results-carousel">
<div class="item item-steve">
<video poster="" id="steve" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/steve.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-chair-tp">
<video poster="" id="chair-tp" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/chair-tp.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-shiba">
<video poster="" id="shiba" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/shiba.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-fullbody">
<video poster="" id="fullbody" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/fullbody.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-blueshirt">
<video poster="" id="blueshirt" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/blueshirt.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-mask">
<video poster="" id="mask" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/mask.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-coffee">
<video poster="" id="coffee" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/coffee.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-toby">
<video poster="" id="toby" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/toby2.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</section> -->
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Summary</h2>
<div class="content has-text-justified">
<!-- Summary -->
<!-- <h3 class="title is-4">"Open the drawer "</h3> -->
<!-- <div class="content has-text-justified">
<p>
Open a drawer task using <span class="dnerf">ForceSight</span>. (Video is sped up x 4)
</p>
</div> -->
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
preload
playsinline
width="100%">
<!-- <source src="./static/videos/open-drawerx4.mp4" -->
<source src="./static/videos/forcesight_summary.mp4"
type="video/mp4">
</video>
</div>
<!--/ Summary -->
<p>
<!-- Prior work has demonstrated that deep models that output kinematic keyframes enable manipulation by real robots with human-interpretable motion goals. Contact forces are critical to manipulation, yet have typically been relegated to lower-level execution during keyframe-driven manipulation. We present ForceSight, a system for multi-task, text-guided mobile manipulation with a deep model that outputs visual and force goals (visual-force keyframes) suitable for visual-force servoing. Given a single RGBD image and a text prompt as input, ForceSight's deep model outputs a visual-force goal, which can be inferred at a high enough rate to work with a moving camera. We evaluate ForceSight using an eye-in-hand RGBD camera on a mobile manipulator. We show that by explicitly representing net applied force and grip force, ForceSight predicts forces suitable to the task, operates more effectively, and provides human-interpretable force goals. -->
We present <span class="dnerf">ForceSight</span>, a transformer-based robotic planner that generates force-based objectives given a text input and an RGBD image, empowering mobile manipulators to plan and execute contact-rich tasks. We demonstrate the utility of <span class="dnerf">ForceSight</span> with 10 mobile manipulation tasks using an eye-in-hand RGBD camera on a mobile manipulator, successfully generalizing to novel environments and unseen object instances. We show that by explicitly predicting force goals, <span class="dnerf">ForceSight</span> predicts forces suitable to the task, operates more effectively, and provides human-interpretable force goals.
</p>
<!-- <p>
Our approach augments neural radiance fields
(NeRF) by optimizing an
additional continuous volumetric deformation field that warps each observed point into a
canonical 5D NeRF.
We observe that these NeRF-like deformation fields are prone to local minima, and
propose a coarse-to-fine optimization method for coordinate-based models that allows for
more robust optimization.
By adapting principles from geometry processing and physical simulation to NeRF-like
models, we propose an elastic regularization of the deformation field that further
improves robustness.
</p>
<p>
We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
photos/videos into deformable NeRF
models that allow for photorealistic renderings of the subject from arbitrary
viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
using a
rig with two mobile phones that take time-synchronized photos, yielding train/validation
images of the same pose at different viewpoints. We show that our method faithfully
reconstructs non-rigidly deforming scenes and reproduces unseen views with high
fidelity.
</p> -->
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Paper video. -->
<!-- <div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Video</h2>
<div class="publication-video">
<iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&showinfo=0"
frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</div>
</div>
</div> -->
<!--/ Paper video. -->
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Tasks -->
<div class="columns is-centered">
<div class="column is-full-width">
<!-- <h2 class="title is-3">Task Samples</h2> -->
<h3 class="title is-4">"Pick up the paperclip" </h3>
<div class="content has-text-centered">
<video autoplay="" loop="" muted="" src="./static/videos/1x3_videos_paperclip.mp4" class="interpolation-image is-centered" alt="Interpolation end reference image."></video>
</div>
<h3 class="title is-4">"Pick up the apple" </h3>
<div class="content has-text-centered">
<video autoplay="" loop="" muted="" src="./static/videos/1x3_videos_apple.mp4" class="interpolation-image is-centered" alt="Interpolation end reference image."></video>
</div>
<h3 class="title is-4">"Open the drawer" </h3>
<div class="content has-text-centered">
<video autoplay="" loop="" muted="" src="./static/videos/1x3_videos_drawer.mp4" class="interpolation-image is-centered" alt="Interpolation end reference image."></video>
</div>
<!-- <h3 class="title is-4">"Turn on the light "</h3>
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/light_atriumx4.mp4"
type="video/mp4">
</video>
</div>
<h3 class="title is-4">"Place object in the hand "</h3>
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/handoverx4.mp4"
type="video/mp4">
</video>
</div>
<h3 class="title is-4">"Pick up the cup "</h3>
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/pick-cupx4.mp4"
type="video/mp4">
</video>
</div>
<h3 class="title is-4">"Place object in the trash "</h3>
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/trashx4.mp4"
type="video/mp4">
</video>
</div>
<h3 class="title is-4">"Pick up the medicine bottle "</h3>
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/medicinex4.mp4"
type="video/mp4">
</video>
</div> -->
</div>
</div>
<!--/ Tasks -->
<div class="columns is-centered">
<!-- Visual Effects. -->
<!-- <div class="column">
<div class="content">
<h2 class="title is-3">Visual Effects</h2>
<p>
Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
would be impossible without nerfies since it would require going through a wall.
</p>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/dollyzoom-stacked.mp4"
type="video/mp4">
</video>
</div>
</div> -->
<!--/ Visual Effects. -->
<!-- Architecture. -->
<div class="column">
<h2 class="title is-3">Model Architecture</h2>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/Architecture.png" class="interpolation-image is-centered" alt="ForceSight architecture"/>
<!-- <video autoplay="" loop="" muted="" src="./static/videos/architecture.mp4" class="interpolation-image is-centered" alt="Interpolation end reference image."></video> -->
<p>
<span class="dnerf">ForceSight</span> is a text-conditioned RGBD vision transformer. An RGBD image is first divided into patches and passed into an RGBD-adapted patch encoder that transforms image patches into image tokens. These image tokens are fed into a vision transformer. After every transformer block inside the vision transformer, the visual features are conditioned on a text embedding via cross-attention to produce text-conditioned image patch features. These patch features are passed into two convolutional decoders to produce an affordance map and a depth map. The patch features are additionally average pooled and passed into several MLPs in order to predict the gripper width, applied force, grip force, and yaw.
</p>
</div>
</div>
</div>
<!--/ Architecture. -->
</div>
<div class="column">
<h2 class="title is-3">System Architecture</h2>
<div class="columns is-centered">
<div class="column content">
<img src="./static/images/sys-archi.png" -->
<!-- <video autoplay="" loop="" muted="" src="./static/videos/high_level.mp4" class="interpolation-image is-centered" alt="Interpolation end reference image."></video> -->
<p>
The <span class="dnerf">ForceSight</span> system architecture comprises several components that work together to accomplish a text-conditioned task. It begins with a high-level task planner, which takes a text input and generates a sequence of action primitives representing subgoals. These action primitives, along with the RGBD input, are then passed to the <span class="dnerf">ForceSight</span> transformer model. This model processes the input and produces force-based objectives. These objectives are subsequently fed into the low-level controller, which generates joint motion commands to reach the next goal. To determine when to switch to the next action primitive, the low-level controller compares the error between the current states and visual-force goals with a predefined threshold. If the error is below the threshold, the low-level controller initiates the switch to the next action primitive. This entire process loop operates at a frequency of 8 Hz.
</p>
</div>
</div>
</div>
<br>
<!-- Force vs No Force -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3"> Visual-Force Goals (Left) vs Kinematic Goals w/o Force (Right)</h2>
<h3 class="title is-4">"pick up the keys" </h3>
<div class="content has-text-centered">
<video autoplay="" loop="" muted="" src="./static/videos/keys_force.mp4" class="interpolation-image is-centered" alt="Interpolation end reference image."></video>
<!-- <video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/open-drawerx4.mp4"
<source src="./static/videos/drawer_cubiclex4.mp4"
<source src="./static/videos/1x3_video_drawer.mp4"
type="video/mp4">
</video> -->
</div>
<h3 class="title is-4">"pick up the paperclip" </h3>
<div class="content has-text-centered">
<video autoplay="" loop="" muted="" src="./static/videos/paperclip_force.mp4" class="interpolation-image is-centered" alt="Interpolation end reference image."></video>
<!-- <video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/open-drawerx4.mp4"
<source src="./static/videos/drawer_cubiclex4.mp4"
<source src="./static/videos/1x3_video_drawer.mp4"
type="video/mp4">
</video> -->
</div>
<h3 class="title is-4">"place object in the hand" </h3>
<div class="content has-text-centered">
<video autoplay="" loop="" muted="" src="./static/videos/handover_force.mp4" class="interpolation-image is-centered" alt="Interpolation end reference image."></video>
<!-- <video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/open-drawerx4.mp4"
<source src="./static/videos/drawer_cubiclex4.mp4"
<source src="./static/videos/1x3_video_drawer.mp4"
type="video/mp4">
</video> -->
</div>
</div>
</div>
<!--/ Dynamic Object Handover -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Dynamic Object Handover</h2>
<h3 class="title is-4">"place object in the hand" </h3>
<div class="content has-text-centered">
<video autoplay="" loop="" muted="" src="./static/videos/hand_tracking.mp4" class="interpolation-image is-centered" alt="Dynamic object handover."></video>
</div>
</div>
</div>
<!--/ Force vs No Force -->
<!-- Continous vs Binary Force -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Continuous Grip Force (Left) vs Binary Gripper Position (Right)</h2>
<h3 class="title is-4">"pick up the cup" </h3>
<div class="content has-text-centered">
<video autoplay="" loop="" muted="" src="./static/videos/cup_force.mp4" class="interpolation-image is-centered" alt="Interpolation end reference image."></video>
<!-- <video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/open-drawerx4.mp4"
<source src="./static/videos/drawer_cubiclex4.mp4"
<source src="./static/videos/1x3_video_drawer.mp4"
type="video/mp4">
</video> -->
</div>
</div>
</div>
<!--/ Emergent Properties -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Emergent Properties</h2>
<h3 class="title is-4">Generalization</h3>
<div class="content has-text-centered">
<img src="./static/images/train_test.png" class="interpolation-image is-centered" alt="Left:train, right:test"/>
<p>
<span class="dnerf">ForceSight</span> is able to generalize to novel environments and unseen object instances. <br>
<b>Left:</b> Objects in the training set. <b>Right:</b> Objects in the test set.
</p>
</div>
</div>
</div>
<br>
<div class="columns is-centered">
<div class="column is-full-width">
<h3 class="title is-4">Agent Agnostic</h3>
<div class="content has-text-centered">
<img src="./static/images/agent_agnostic.png" class="interpolation-image is-centered" alt="ForceSight is agent-agnostic"/>
<p>
Predictions from <span class="dnerf">ForceSight</span> are agnostic to the agent and camera perspective, as shown in this example for the apple grasping task.
</p>
</div>
</div>
</div>
<br>
<div class="columns is-centered">
<div class="column is-full-width">
<h3 class="title is-4">Multi-step prediction</h3>
<div class="content has-text-centered">
<img src="./static/images/multistep_prediction.png" class="interpolation-image is-centered" alt="Multistep prediction" style="width: 75%;"/>
<p>
<span class="dnerf">ForceSight</span> is able to make reasonable predictions for action primitives that are more than one keyframe into the future, despite having been trained to predict goals associated with only the next keyframe.
</p>
</div>
</div>
</div>
<!--/ Assigning action primitives with an LLM -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Assigning action primitives with an LLM</h2>
<div class="content has-text-centered">
<!-- <video autoplay="" loop="" muted="" src="./static/videos/hand_tracking.mp4" class="interpolation-image is-centered" alt="Dynamic object handover."></video> -->
<img src="./static/images/gpt4_output.png" class="interpolation-image is-centered" alt="GPT-4 output"/>
<p>
We demonstrate how a large language model (GPT-4) could plausibly be used to assign action primitives to task descriptions.
</p>
</div>
</div>
</div>
<!-- / Assigning action primitives with an LLM -->
<!-- <div class="column">
<h2 class="title is-3">Assigning action primitives with an LLM</h2>
<div class="columns is-full-width">
<div class="column content">
<img src="./static/images/gpt4_output.png" class="interpolation-image is-centered" alt="GPT-4 output"/>
<p>
We demonstrate how a large language model (GPT-4) could plausibly be used to assign action primitives to task descriptions.
</p>
</div>
</div>
</div> -->
<!-- Concurrent Work. -->
<!-- <div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Related Links</h2>
<div class="content has-text-justified">
<p>
There's a lot of excellent work that was introduced around the same time as ours.
</p>
<p>
<a href="https://arxiv.org/abs/2104.09125">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
</p>
<p>
<a href="https://www.albertpumarola.com/research/D-NeRF/index.html">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/">NR-NeRF</a>
both use deformation fields to model non-rigid scenes.
</p>
<p>
Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/">NSFF</a>, and <a href="https://neural-3d-video.github.io/">DyNeRF</a>
</p>
<p>
There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF">Yen-Chen Lin's curated list of NeRF papers</a>.
</p>
</div>
</div>
</div> -->
<!--/ Concurrent Work. -->
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@misc{collins2023forcesight,
title={ForceSight: Text-Guided Mobile Manipulation with Visual-Force Goals},
author={Jeremy A. Collins and Cody Houff and You Liang Tan and Charles C. Kemp},
year={2023},
eprint={2309.12312},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
</code></pre>
</div>
</section>
<footer class="footer">
<div class="container">
<div class="content has-text-centered">
<a class="icon-link"
href="./static/paper/ForceSight.pdf">
<i class="fas fa-file-pdf"></i>
</a>
<a class="icon-link" href="https://github.com/force-sight" class="external-link" disabled>
<i class="fab fa-github"></i>
</a>
</div>
<div class="columns is-centered">
<div class="column is-8">
<div class="content">
<p>
This website is licensed under a <a rel="license"
href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
Commons Attribution-ShareAlike 4.0 International License</a>.
</p>
<p>
This website is forked from <a
href="https://github.com/nerfies/nerfies.github.io">Nerfies</a>.
</p>
</div>
</div>
</div>
</div>
</footer>
</body>
</html>